Interested in this AI/ML Engineer role at Northern Trust Corp.?
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About This Role
About Northern Trust
As a global leader in innovative wealth management, asset servicing, asset management and banking services, Northern Trust (Nasdaq: NTRS) is proud to guide the world’s most successful individuals, families, corporations and institutions.
Since 1889, we have aligned our efforts with our three guiding Principles That Endure: Service, Expertise, and Integrity. Together, they reflect the three cornerstones of business conduct which we strive to instill in our employees, whom we call partners, and to provide to our clients and the communities we serve worldwide.
With more than 135 years of financial experience and over 24,000 partners, we serve the world’s most sophisticated clients using leading technology and exceptional service.
Job Description Summary
As a key member of our AI Engineering team who builds, ships, and owns production\-grade generative AI systems, the lead the design and implementation of secure, scalable, and compliant AI platform powered by LLM’s\-powered on Azure AI Foundry, working across multiple model providers (Claude / Anthropic, GPT / OpenAI, and others), applying modern AI design patterns (RAG, agents, tool use, orchestration) to solve real business problems in a highly regulated financial environment.
Job Description
1\. Lead development of a portfolio of client\-facing AI capabilities and integration methods, embedding them into client front ends and interactive dashboards to ensure these capabilities are well aligned and meet required validation and responsible AI standards.
2\. Develop and oversee business unit wise approach to integrate structured and unstructured data into NT’s enterprise AI framework in collaboration with the AI architecture, AI engineering, and data platform engineering teams.
3\. Develop and lead the practice of data pipeline engineering, rationalizing custom data integrations over time to establish common methods and approaches.
4\. Architect and implement data pipelines that integrate structured and unstructured data from internal banking systems, external feeds, and cloud platforms for AI/ML use cases.
5\. Drive our semantic architecture and engineering approach for Northern Trust intelligence to advance enterprise context engineering and architecture disciplines.
6\. Collaborate with the AI consulting team, business units, data scientists, model risk teams, and other stakeholders to understand data requirements for AI models supporting key use cases such as portfolio management, quants \& research, reconciliations, and customer intelligence; drive engineering specifications and delivery for these capabilities.
7\. Ensure sound practices are executed to deliver and maintain metadata management, data lineage, and audit trails for AI data assets as development and data teams expand AI data access.
8\. Provide leadership to multiple Technical Delivery teams to prioritize and refine critical data deliverables based on business use cases and high\-value data assessments.
9\. Work closely on vendor negotiations and guide data solutions with procurement and transformation teams to enable scalable consumption with favorable and appropriate terms and capabilities, including understanding support or vendor SLA requirements.
10\. Provide guidance for regulatory compliance processes, audits, and internal reviews.
11\. Act as End\-to\-End Data Integration Product Management Vision and Roadmap leader in support of AI transformation programs.
12\. Support real\-time and batch data ingestion from asset management vendor platforms i.e. Aladdin, Eagle, iCapital, and third\-party APIs.
13\. Optimize data workflows for performance, reliability, and cost\-efficiency across hybrid cloud environments.
14\. Partner with cybersecurity and compliance teams to ensure data privacy, encryption, and access controls are enforced.
15\. Contribute to the development of enterprise\-wide AI architecture standards and governance frameworks, ensuring secure and efficient data consumption.
16\. Provide indirect leadership across lateral teams of data analysts focused on improving data accountability, access control, and user enablement.
17\. Provide mentoring and coaching to senior, mid\-level, and junior engineers, product managers, and architects; function as a team lead as required as the AI and AI data integration practice is scaled.
Knowledge
- Strong proficiency in Python, SQL, VS Code, GitHub Copilot, and data integration tools (e.g., SQL, Pyspark, experience with cloud platforms (Azure, AWS, GCP) and container architecture.
- Hands\-on experience building LLM / GenAI applications production grade AI patterns like MCP, Agentic AI, RAG, NLP2SQL etc.
- Solid SQL and PostgreSQL skills; experience with PGVector or a comparable vector database (Pinecone, Neo4j etc.).
- Comfort designing and consuming REST APIs; understanding of authentication, rate limiting, and error handling.
- Working knowledge of at least one major model provider API—Anthropic (Claude) and/or OpenAI (GPT)—including prompt design, function/tool calling, and streaming.
- Experience with AI/ML frameworks and AI concepts.
- Familiarity with financial data providers (e.g., Bloomberg, Refinitiv, Factset etc.).
- Deep understanding of and asset management domain, data governance, regulatory compliance, and risk management in banking.
- Excellent communication and stakeholder management skills.
- Understanding of SQL, Oracle, Exadata, Snowflake, Data Bricks, etc..
- Understanding of development Languages: Python , .NET/C\#, React, and Java
- Understanding of operating systems: Unix/BASH Shell, Windows, Linux OS
- Proficient in generative AI and LLM data preparation for financial use cases.
Experience
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
- 10\+ years of experience in data engineering or integration, preferably in financial services or banking.
Salary Range:
$137,400 \- 233,600 USD*Salary range is a good faith estimate of base pay. Northern Trust provides a comprehensive benefits package including retirement benefits (401k and pension), health and welfare benefits (medical, dental, vision, spending accounts and disability), paid time off, parental and caregiver leave, life \& accident insurance, and other voluntary and well\-being benefits. Northern Trust also provides a discretionary bonus program that may include an equity component.*
Work Authorization
Applicants must be authorized to work in the U.S. without the need for employment\-based visa sponsorship now or in the future. Northern Trust will not sponsor applicants for U.S. work visa status for this opportunity (no sponsorship is available for H\-1B, L\-1, TN, O\-1, E\-3, H\-1B1, F\-1, J\-1, OPT, CPT or any other employment\-based visa).
Working with Us
As a Northern Trust partner, you will be part of a flexible and collaborative work culture, which has a strong history of financial strength and stability. Movement within the organization is encouraged, senior leaders are accessible, and you can take pride in working for a company committed to an inclusive workplace and assisting the communities we serve.
Philanthropy is deeply rooted in Northern Trust’s history and is an essential element of our culture. Employees around the world give their time and talent to work for the greater good of their communities.
Reasonable Accommodation
Northern Trust is committed to working with and providing adjustments to individuals with health conditions and disabilities. If you need a reasonable accommodation for any part of the employment process, please email our HR Service Center at MyHRHelp@ntrs.com, or alternatively you can discuss your individual requirements with the recruiter you are working with.
Salary Context
This $137K-$233K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Northern Trust Corp., this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($185K) sits 15% below the category median. Disclosed range: $137K to $233K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Northern Trust Corp. AI Hiring
Northern Trust Corp. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $233K - $233K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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